There is a tendency in RTD
to focus on technology (particularly what might loosely be called AI) without
having a clear idea of what it is to be used for or how to measure its
effectiveness. This may be coupled with attempts to transfer technologies that
haven't worked well in the past into a new area as though they were new ideas.
Groups that want to re-label their work in this way should have to provide an
ab initio justification for funding in KM. More importantly RTD of this sort
should be linked closely to a working KM environment in some organisation
outside the research group so that some measure of success is generated not
just by the research workers but by the users. The failure of generic solutions
to show any success in KM provides the principal justification for being wary
of proposals for such research.

Some of the more exciting
ideas that are being proposed by the 'machine intelligence' community need to
be carefully assessed, particularly in relation to 'automated' systems. It is
difficult to automate something that is not well understood. There is very
little evidence that automation works in cognitive science if we look back at
the development record of, for example, natural language understanding. There
is a need to orient this type of work towards feasibility studies and to
identify key problems in projects which if not solved quickly will invalidate
the whole approach (such as scalability).

The importance of standards
such as XML lies in their use in achieving interoperability and interconnection
of applications and basic knowledge stores. One of the problems with
standardising terminology, however, is the proliferation of incompatible
'views' represented in alternative ontologies and DTDs, a problem that was
identified with SGML and has yet to be properly addressed. This latter
difficulty is already partially visible in the problem of persuading people to
take an active part in KM projects where sharing implies changing individual
views. Another long-term difficulty lies in the cost of creating the mark-up
that is the key to the effective use of computerised systems. Again this was a
problem identified with SGML work, probably the main reason for its commercial
failure. The idea that semantic mark-up can be generated automatically has not
been demonstrated. Indeed, if it were possible, it is difficult to see the need
for it, as the methodology could be applied directly to solve the problems
mark-up is intended to avoid.

KM requires a different
perspective to IT and it is not sufficient to simply re-iterate the need for
usability, interoperability and standards. Their place in KM requires a
re-focussing of effort and even perhaps, methods and concepts. Standards like
Dublin core for example are essentially information-centric rather than
knowledge-centric. Groups working in this area might consider how to develop a
new focus for some of their work.

Candidates for large
projects might be sector level efforts to evaluate the use of experimental KM
methods. The sort of project grouping and development that the GEN
(engineering) projects created would be a good example (transferred to the KM
area). As sharing knowledge between suppliers, manufacturers and customers is
one of the aims of KM, a community such as European aero-space engineering
might be a suitable group. One of the aims of such projects should be to share
knowledge about KM effectiveness.

File downloads
from the El.pub site are currently suspended - the links however have not
been updated to reflect this. If you would like access to a particular download
file - please email webmasters@elpub.org
with a suitable request confirming a description of the file you wish to
download.

El.pub - Interactive
Electronic Publishing R & D News and ResourcesWe welcome feedback
and contributions to the information service, and proposals for subjects for
the news service (mail to: webmasters@elpub.org)